%load_ext pretty_jupyter

The pretty_jupyter extension is already loaded. To reload it, use:
  %reload_ext pretty_jupyter

Introduction

In diesem Notebook wenden wir Applied Machine Learning (AML) Techniken an, um effektive Strategien für personalisierte Kreditkarten-Werbekampagnen zu entwickeln. Unser Ziel ist es, mithilfe von Kunden- und Transaktionsdaten präzise Modelle zu erstellen, die die Wahrscheinlichkeit des Kreditkartenkaufs vorhersagen.

Lib Importing

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from itables import init_notebook_mode

init_notebook_mode(all_interactive=True)

Load the Data

account = pd.read_csv("account.csv", sep=";")
card = pd.read_csv("card.csv", sep=";")
client = pd.read_csv("client.csv", sep=";")
disp = pd.read_csv("disp.csv", sep=";")
district = pd.read_csv("district.csv", sep=";")
loan = pd.read_csv("loan.csv", sep=";")
order = pd.read_csv("order.csv", sep=";")
trans = pd.read_csv("trans.csv", sep=";", low_memory=False)

EDA

Account

account
account_id district_id frequency date
Loading... (need help?)

Card

card
card_id disp_id type issued
Loading... (need help?)

Client

client
client_id birth_number district_id
Loading... (need help?)

Disp

disp
disp_id client_id account_id type
Loading... (need help?)

District

district
A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13 A14 A15 A16
Loading... (need help?)

Loan

loan
loan_id account_id date amount duration payments status
Loading... (need help?)

Order

order
order_id account_id bank_to account_to amount k_symbol
Loading... (need help?)

Trans

trans
trans_id account_id date type operation amount balance k_symbol bank account
Loading... (need help?)

Transformations

Account

Card

Client

Disp